Single-photon imaging uses single-photon-sensitive picosecond-resolution sensors to capture 3D structure and supports diverse applications, but success remains mostly limited to simple scenes. In complex scenarios, traditional methods degrade and deep learning methods lack flexibility and generalization. Here, we propose a physics-informed deep neural network (PIDNN) framework that effectively addresses both aspects, adapting to complex and variable sensing environments by embedding imaging physics into the deep neural network for unsupervised learning. Within this framework, by tailoring the number of U-Net skip connections, we impose multi-scale spatiotemporal priors that improve photon-utilization efficiency, laying the foundation for addressing the inherent low-signal-to-background ratio (SBR) problem in subsequent complex scenarios. Additionally, we introduce volume rendering into the PIDNN framework and design a dual-branch structure, further extending its applicability to multiple-depth and fog occlusion. We validated the performance of this method in various complex environments through numerical simulations and real-world experiments. The results of photon-efficient imaging with multiple returns show robust performance under low SBR and large fields of view. The method attains lower root mean-squared error than traditional methods and exhibits stronger generalization than supervised approaches. Further multiple depths and fog interference experiments confirm that its reconstruction quality surpasses existing techniques, demonstrating its flexibility and scalability. Both simulation and experimental results validate its exceptional reconstruction performance and flexibility.

Infrared and visible image fusion (IVIF) significantly enhances scene interpretation by integrating broad-spectrum information. Drawing inspiration from specific snakes that possess an evolutionarily optimized bimodal sensory system capable of parallel processing infrared and visible radiation, we propose a novel IVIF framework incorporating two key elements: nonlinear cross-modal interactions across six distinct classes of snake bimodal neurons and dynamic center-surround receptive field organization. These biological principles are mathematically formalized and integrated within a deep neural network (DNN), optimized through an object detection region-guided loss and a frequency-dependent fusion loss that enable data-driven fusion strategy learning. Experimental results demonstrate that the optimized model effectively emulates the infrared-visible information integration observed in snake bimodal neurons. Critically, the nonlinear bimodal neurons capture a significantly greater amount of edge information and finer mid-to-high-frequency details, which are essential for the subsequent reconstruction of the fused image. Furthermore, a comprehensive evaluation of visual quality, encompassing both qualitative and quantitative assessments on six datasets, along with extensive object detection and semantic segmentation experiments using the fused images in both daytime and nighttime scenarios, demonstrates that our model outperforms traditional biologically-inspired IVIF algorithms, achieving performance comparable to SOTA DNN-based methods. The code and weights are available at https://github.com/rwerwer2024/SBNF.

The current spiking neural network (SNN) relies on spike-timing-dependent plasticity (STDP) primarily for shape learning in object recognition tasks, overlooking the equally critical aspect of color information. To address this gap, our study introduces an unsupervised variant of STDP that incorporates principles from color-opponency mechanisms (COM) and classical receptive fields (CRF) found in the biological visual system, facilitating the integration of color information during parameter updates within the SNN architecture. Our approach initially preprocesses images into two distinct feature maps: one for shape and another for color. Then, signals derived from COM and intensity concurrently drive the STDP process, thereby updating parameters associated with both color and shape feature maps. Furthermore, we propose a channel-wise attention mechanism to enhance differentiation among objects sharing similar shapes or colors. Specifically, this mechanism utilizes convolution to generate an output spike-wave, identifying a winner based on earliest spike timing and maximal potential. The winning kernel computes attention, which is then applied via convolution to each input image feature map, generating post-feature maps. A STDP-like normalization rule compares firing times between pre- and post-feature maps, dynamically adjusting channel weights to optimize object recognition during the training phase. We assessed the proposed algorithm using SNN with both single-layer and multi-layer architectures across three datasets. Experimental findings highlight its efficacy and superiority in complex object recognition tasks compared to state-of-the-art (SOTA) algorithms. Notably, our approach achieved a significant 20% performance improvement over the SOTA on the Caltech-101 dataset. Moreover, the algorithm is well-suited for hardware implementation and energy efficiency, leveraging a winner-selection mechanism based on the earliest spike time. • Bio-inspired STDP learning, incorporating color-opponency and receptive fields, updates synaptic weights. • Channel-wise attention dynamically adjusts color and luminance channel weights. • Outperforms state-of-the-art single and multi-layer SNNs on complex object recognition (Caltech 101).

Object detection approaches are expanding by leaps and bounds with recent progress in deep learning. However, there is a considerable amount of environments hampering and challenging generic detectors in open-world scenarios, which received quite limited attention. In this paper, we focus on three specific challenging conditions: 1) targets presented with low lightness, 2) camouflaged objects merged in backgrounds, 3) complex acquisition scenarios, and present a novel end-to-end detector accordingly, termed Context-awareness Network (CANet). Specifically, we propose Global Context Encoder and Context Feature Fusion module to model the context-awareness (CA) mechanism that plays a crucial role in the human visual system (HVS) in an explicit way, which integrates both latent global and local context information to make each region of interest (RoI) more informative, and thus more discriminative. To our knowledge, such high-level mechanisms are under-explored for object detection in the literature. In addition, Global Semantic Awareness module is designed to regress positions and classify better in the process of extracting the feature. Experiments demonstrate that CANet achieves very competitive performance on the ExDark, DARK FACE, COD10K, and CURE-TSD, suggesting the effectiveness and efficiency of CANet in various challenging conditions as well as common scenarios.

Estimating the illuminant from a color-biased image is an ill-posed problem without prior information or invariance about the surfaces of a scene. Based on the classical image formation model, we have developed a heuristic approach to obtain the illuminant color by computing the ratio of the average of all pixels over the color-biased scene to that over the roughly recovered scene obtained by local normalization in each channel. The computed ratio represents an estimated invariance across color channels (IACC), ranging between the average reflectance of surfaces and the maximum reflectance of surfaces of a scene, modulated by the illuminant color. This work builds a mathematical foundation for IACC and explains why it is suitable for illuminant estimation. The core discovery is that the magnitude relationship of the average reflectances of surfaces between any two color channels is opposite to that of the maximum reflectances of surfaces for most natural scenes. As a result, we have designed two approaches for explicitly learning IACC of an image, resulting in very accurate illuminant estimation. The first approach involves a novel shallow model based on diagonal or non-diagonal matrices, together with the learned model parameters, to improve IACC performance. The second approach applies IACC as a constraint to optimize a novel deep learning approach, which has achieved state-of-the-art performance on two benchmarks. An interesting finding is that the output of the learned network, constrained only by IACC loss, provides a coarse estimation of intrinsic images such as albedo from the input color-biased image

Previous work has demonstrated that incorrect white balance (WB) in the camera image signal processing pipeline has a negative impact on the performance of deep neural networks (DNNs) in high-level vision tasks, and traditional image augmentation approaches are not well suited for modeling WB errors. However, it is still unclear when this impact will occur for which kinds of images and objects. In this paper, we manually labeled 2304 images from the RECommended dataset and NUS dataset and discovered that the effect of WB on DNNs is greatly associated with object size and occlusion level among objects. In images with incorrect WB, small objects and objects with heavily occluded backgrounds are the main factors resulting in the bad performance of DNNs, indicating that the effect of WB is clearly associated with the shape of objects. Our findings may support that the functional role of some neurons in the visual cortex (e.g., V1 or V4 areas) realizing color constancy (CC) and encoding object attributes such as color and shape dependently is to contribute to high-level vision. Furthermore, based on this scientific finding, we proposed a novel augmentation strategy to address the negative impact of incorrect WB by expanding the training datasets in both color transformation and synthetic occlusion. We compared our proposed strategy with the current augmentation strategies and showed that our approach clearly improves the performance of DNNs in detection and segmentation tasks with small objects and objects with heavily occluded backgrounds.

Image fidelity refers to the ability of a process to render an image accurately. As image enhancement algorithms have been developed in recent years, how to assess the performances of different image enhancement algorithms has become an important question. Some objective image quality assessment (IQA) methods have been proposed, but there is little research on the image fidelity evaluation when comparing the performances of enhancement algorithms. Therefore, the authors proposed a new image fidelity assessment framework consisting of three components: the information entropy fidelity, constituent fidelity and colour fidelity. To verify the rationality of the fidelity criteria, they used the popular IQA database (LIVE), and the results indicated that the method matched better with the subjective assessment. Then, they verified the effectiveness of their method with the famous technique of image enhancement: multi‐scale Retinex with colour restoration (MSRCR). The experimental results demonstrate MSRCR can improve the image quality, but it gives rise to obvious distortions. It is necessary to keep a moderate balance between image fidelity and image quality when they assess the enhanced images. Their results showed that the proposed objective fidelity index could provide an additional objective basis for the quality evaluation of image enhancement algorithms.

Long Short-Term Memory (LSTM) is a popular approach to boosting the ability of Recurrent Neural Networks to store longer term temporal information. The capacity of an LSTM network can be increased by widening and adding layers. However, usually the former introduces additional parameters, while the latter increases the runtime. As an alternative we propose the Tensorized LSTM in which the hidden states are represented by tensors and updated via a cross-layer convolution. By increasing the tensor size, the network can be widened efficiently without additional parameters since the parameters are shared across different locations in the tensor; by delaying the output, the network can be deepened implicitly with little additional runtime since deep computations for each timestep are merged into temporal computations of the sequence. Experiments conducted on five challenging sequence learning tasks show the potential of the proposed model.

Color information plays an important role in better understanding of natural scenes by at least facilitating discriminating boundaries of objects or areas. In this study, we propose a new framework for boundary detection in complex natural scenes based on the color-opponent mechanisms of the visual system. The red-green and blue-yellow color opponent channels in the human visual system are regarded as the building blocks for various color perception tasks such as boundary detection. The proposed framework is a feed forward hierarchical model, which has direct counterpart to the color-opponent mechanisms involved in from the retina to the primary visual cortex (V1). Results show that our simple framework has excellent ability to flexibly capture both the structured chromatic and achromatic boundaries in complex scenes.

The double-opponent color-sensitive cells in the primary visual cortex (V1) of the human visual system (HVS) have long been recognized as the physiological basis of color constancy. We introduce a new color constancy model by imitating the functional properties of the HVS from the retina to the double-opponent cells in V1. The idea behind the model originates from the observation that the color distribution of the responses of double-opponent cells to the input color-biased images coincides well with the light source direction. Then the true illuminant color of a scene is easily estimated by searching for the maxima of the separate RGB channels of the responses of double-opponent cells in the RGB space. Our systematical experimental evaluations on two commonly used image datasets show that the proposed model can produce competitive results in comparison to the complex state-of-the-art approaches, but with a simple implementation and without the need for training.